In this tutorial you're going to learn about quota sampling. Now quota sampling is somewhat similar to stratified random sampling. But it does have some key differences that we should understand.
Now like a stratified random sampling the sampling frame which is all the people, or individuals, or subjects who could be sampled is broken down into smaller subgroups. These subgroups could be gender, or race, or graduating class in a high school, or something. And so we can see the differences between those. However, unlike stratified random sampling quota sampling deals with selecting some predetermined number from each group but doesn't need to be random.
Now this can have some major drawbacks. And we'll discuss those in a little bit.
So typically the purpose of quota sampling is to ensure that groups in the sample are roughly proportionally represented in the sample. Much as they are in the population. So, for example, suppose that this high school has just adopted some new healthy lunch options. And they want to solicit student feedback on them. The school has 100 freshman, 110 sophomores, 120 juniors, and 90 seniors. And they want to sample 10% of the school to obtain feedback on that. How would you do that?
One way to do it would be to just sample 10% of each class so 10 freshman, 11 sophomores, 12 juniors, and 9 seniors. It's not important how you get these students. Simply just that you get this many of each. And that's where the term quota comes from.
Now suppose you took the first 10 freshman, 11 sophomores, et cetera that entered the lunchroom. You might then over-represent the percent of students that like the lunch options because they happened to be the first ones to lunch. That probably means that they like the lunch options maybe more than the average student. And so the results of your sample might over represent systematically the percent of students that enjoy these options. That would be a biased sample. Because you didn't randomly select. You did it sort of as a convenient sample at lunchtime where the students were easy to obtain.
So the benefits of doing a quota sample is that in a pinch it's quick to set up and easy to do. So it's easy to conduct. Also the information gets to be broken down by category. So you could break it down by demographic information. Or in the previous example you could break it down as to whether sophomores prefer the lunch options more than seniors do.
But the biggest drawback is that since the selection process isn't random you might not be able to generalize the results of your study to the population. It might not be representative of the population. And this is a big problem. I cannot emphasize enough how big of a problem this is. Our goal is to be able to generalize the results of our study to the population at large. And if the selection process isn't random there's no real guarantee. In fact, all we can do is sort of hope that our sample is representative of the larger population. And so I would strongly suggest that, if possible, you use a stratified random sample as opposed to a quota sample.
So to recap in quota sampling the similarities to stratified random sampling are that the population is broken down into strata. Then a sample is taken from within each of those strata. And the biggest difference though is the samples within the strata don't have to be randomly selected. And the biggest drawback there is that may affect your ability to generalize your results to the population.
So we talked about quota sampling and how it's kind of a not as good version of stratified random sampling. Good luck. And we'll see you next time.
A sampling method where a certain, predetermined number of individuals are taken from each of several different classes of the population. The selection method does not need to be random, which may not result in a representative sample of the population.